- EN.601.461, Computer Vision
- EN.601.457, Computer Graphics
- AS.050.675 Probabilistic Models of the Visual Cortex
- EN.601.482, Machine Learning: Deep Learning
- EN.520.433, Medical Image Analysis
- EN.601.783 Vision as Bayesian Inference
- EN.520.665 Machine Perception
- EN.520.650 Machine Intelligence
- EN.520.438/638 Deep Learning
- EN.520.344 Introduction to Digital Signal Processing
EN.601.461, Computer Vision – Fall Semester (Katyal)
Course Description:
This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3-D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine vision and biological vision are also included.
EN.601.457, Computer Graphics – Fall Semester (Kazhdan)
Course Description:
This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications]
AS.050.675 Probabilistic Models of the Visual Cortex – Fall Semester (Yuille)
Course Description:
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Also offered as AS.050.375.
EN.601.482, Machine Learning: Deep Learning – Spring Semester (Unberath)
Course Description:
Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics. The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics. Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications]
EN.520.433, Medical Image Analysis – Spring Semester (Prince)
Course Description:
This course covers the principles and algorithms used in the processing and analysis of medical images. Topics include, interpolation, registration, enhancement, feature extraction, classification, segmentation, quantification, shape analysis, motion estimation, and visualization. Analysis of both anatomical and functional images will be studied and images from the most common medical imaging modalities will be used. Projects and assignments will provide students experience working with actual medical imaging data.
EN.601.783 Vision as Bayesian Inference – Spring Semester (Yuille)
Course Description:
This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and non-linear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. [Analysis or Applications] Required course background: calculus, linear algebra (AS.110.201 or equiv.), probability and statistics (AS.553.311 or equiv.), and the ability to program in Python and C++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475) suggested but not required.
EN.520.665 Machine Perception – Fall Semester (Chellappa)
Course Description:
This course will cover topics such as Marr-Hildreth and Canny edge detectors, local representations (SIFT, LBP), Markov random fields and Gibbs representations, normalized cuts, shallow and deep neural networks for image and video analytics, shape from shading, Make 3D, stereo, and structure from motion.
EN.520.650 Machine Intelligence – Spring Semester (Chellappa)
Course Description:
This course will cover most topics studied in artificial intelligence, with emphasis on the “core competences” of intelligent systems – search, knowledge representation, reasoning under uncertainty, vulnerability, ethics and safety of intelligent systems. Recent applications in engineering and medicine will be highlighted.
EN.520.438/638 Deep Learning – Spring Semester (Patel)
Course Description:
Deep Learning is emerging as one of the most successful tools in machine learning for feature learning and classification. This course will introduce students to the basics of Neural Networks and expose them to some cutting-edge research. In particular, this course will provide a survey of various deep learning-based architectures such as autoencoders, recurrent neural networks and convolutional neural networks. We will discuss merits and drawbacks of available approaches and identify promising avenues of research in this rapidly evolving field. Various applications related to computer vision and biometrics will be studied. The course will include a project, which will allow students to explore an area of Deep Learning that interests them in more depth.
EN.520.344 Introduction to Digital Signal Processing – Fall Semester (Patel)
Course Description:
Introduction to digital signal processing, sampling and quantization, discrete time signals and systems, convolution, Z-transforms, transfer functions, fast Fourier transform, analog and digital filter design, A/D and D/A converters, and applications of DSP.